Transfer Learning and Deep Domain Adaptation

Xu, Wen; He, Jing; Shu, Yanfeng

doi:10.5772/intechopen.94072

Cited by 24 publications

(12 citation statements)

References 41 publications

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“…However, transfer learning includes scenarios where the target domain's feature space differs from the source feature space or spaces 169 , 170 , 171 . The intuition behind this is that deep neural networks have a lot of capacity to learn representations from a single dataset, and some of that information can be reused for future tasks [172] . Such approaches could be adopted when there is a shortage of training data.…”

Section: Covid-19 Ct Diagnosis By Weakly Supervised Learningmentioning

confidence: 99%

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review

Hassan¹,

Ren²,

Zhou³

et al. 2022

Computer Methods and Programs in Biomedicine

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Section: Covid-19 Ct Diagnosis By Weakly Supervised Learningmentioning

confidence: 99%

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review

Hassan¹,

Ren²,

Zhou³

et al. 2022

Computer Methods and Programs in Biomedicine

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“…[81] VGG19 architecture with a logistic regression classifier Folio 96% 96% 99%, Flavia Swedish leaf datasets [82] AousethNet Mendeley dataset (MD2020 99% Bridelia ferruginea (6) Baphia nitida (7) Bidens pilosa ( 8) Blighia sapidia (9) Cassia alata (10) Clausena anisata (11) Citrus aurantifolia (12) Capparis erythrocarpus (13) Cnestis ferruginea (14) Cassia occidentalis (15) Chromolaena odorata (16) Carapa procera (17) Cryptolepis sanguinolente (18) Desmodium adscendens (19) Dialium guineense (20) Datura metel (21) Ficus asperifolia (22) Fleurya aestuans (23) Griffonia simplicifolia (24) Hoslundia Opposita (25) Kigelia africana (26) Khaya senegalensis (27) Lantana Camara (28) Momordica charantia (29) Mangifera indica (30) Morinda Lucida (31) Monodora myristica (32) Mondia whitei (33) Nauclea Latifolia (34) Newbouldia laevis (35) Ocimu gratissimum (36) Physalis angulata (37) Palisota hirsuta (38) Parquentina nigrescens (39) Phyllantus nururi (40) Plumbago zeylanica (41) Passiflora foetida (42) Ricinus communis (43) Rauwolfia vormitoria (44) Sida acuta (45) Synedrella nodiflora (46) Trema orientalis …”

Section: Log Gabor Filters David Field Proposed the Log-mentioning

confidence: 99%

A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms

Oppong

Twum

Hayfron-Acquah

et al. 2022

Computational Intelligence and Neuroscience

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Computer vision is the science that enables computers and machines to see and perceive image content on a semantic level. It combines concepts, techniques, and ideas from various fields such as digital image processing, pattern matching, artificial intelligence, and computer graphics. A computer vision system is designed to model the human visual system on a functional basis as closely as possible. Deep learning and Convolutional Neural Networks (CNNs) in particular which are biologically inspired have significantly contributed to computer vision studies. This research develops a computer vision system that uses CNNs and handcrafted filters from Log-Gabor filters to identify medicinal plants based on their leaf textural features in an ensemble manner. The system was tested on a dataset developed from the Centre of Plant Medicine Research, Ghana (MyDataset) consisting of forty-nine (49) plant species. Using the concept of transfer learning, ten pretrained networks including Alexnet, GoogLeNet, DenseNet201, Inceptionv3, Mobilenetv2, Restnet18, Resnet50, Resnet101, vgg16, and vgg19 were used as feature extractors. The DenseNet201 architecture resulted with the best outcome of 87% accuracy and GoogLeNet with 79% preforming the worse averaged across six supervised learning algorithms. The proposed model (OTAMNet), created by fusing a Log-Gabor layer into the transition layers of the DenseNet201 architecture achieved 98% accuracy when tested on MyDataset. OTAMNet was tested on other benchmark datasets; Flavia, Swedish Leaf, MD2020, and the Folio dataset. The Flavia dataset achieved 99%, Swedish Leaf 100%, MD2020 99%, and the Folio dataset 97%. A false-positive rate of less than 0.1% was achieved in all cases.

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“…Transfer learning reuses prior knowledge to improve the learning efficiency or performance in new tasks [28,30]. In reinforcement learning, higher-level knowledge such as macro actions, skills and lower-level knowledge such as reward functions, policies could be transferred.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning and Curriculum Learning in Sokoban

Zhao

Preuß

Plaat

2022

Communications in Computer and Information Science

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Transfer learning can speed up training in machine learning, and is regularly used in classification tasks. It reuses prior knowledge from other tasks to pre-train networks for new tasks. In reinforcement learning, learning actions for a behavior policy that can be applied to new environments is still a challenge, especially for tasks that involve much planning. Sokoban is a challenging puzzle game. It has been used widely as a benchmark in planning-based reinforcement learning. In this paper, we show how prior knowledge improves learning in Sokoban tasks. We find that reusing feature representations learned previously can accelerate learning new, more complex, instances. In effect, we show how curriculum learning, from simple to complex tasks, works in Sokoban. Furthermore, feature representations learned in simpler instances are more general, and thus lead to positive transfers towards more complex tasks, but not vice versa. We have also studied which part of the knowledge is most important for transfer to succeed, and identify which layers should be used for pre-training (Codes we used for this work can be found at https://github.com/yangzhao-666/TLCLS).

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Transfer Learning and Deep Domain Adaptation

Cited by 24 publications

References 41 publications

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review

A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms

Transfer Learning and Curriculum Learning in Sokoban

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